JOURNAL ARTICLE
Debates on the dorsomedial prefrontal/dorsal anterior cingulate cortex: insights for future research.
Published In: Brain: A Journal of Neurology, 2023, v. 146, n. 12. P. 4826 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Clairis, Nicolas; Lopez-Persem, Alizée 3 of 3
Abstract
This article focuses on the dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC), a brain region with ill-defined anatomical borders and associated with a wide range of cognitive functions. It reviews major theories explaining dmPFC/dACC functions, including cognitive control models (conflict monitoring and expected value of control), error likelihood and prediction error models (such as the predicted response-outcome and hierarchical error representation models), and the foraging value theory, as well as the multiple signals view (MSV) which posits that the dmPFC/dACC supports several independent functions. Despite disagreements over which theory best accounts for dmPFC/dACC activity, all agree that this region plays a central role in goal-directed behavior and signals the need for behavioral adaptation. The article highlights ongoing debates about anatomical variability, functional heterogeneity, and the integration of multiple signals, and suggests future research directions involving electrophysiology, individual anatomical differences, connectivity analyses, and artificial intelligence-inspired models to better understand the dmPFC/dACC's complex role.
Additional Information
- Source:Brain: A Journal of Neurology. 2023/12, Vol. 146, Issue 12, p4826
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2023
- ISSN:0006-8950
- DOI:10.1093/brain/awad263
- Accession Number:174108468
- Copyright Statement:Copyright of Brain: A Journal of Neurology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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